Toward robust quantification of dopamine and serotonin in mixtures using nano-graphitic carbon sensors

• Published in: Analyst (London) Vol. 149; no. 8; pp. 2351 - 2362
• Main Authors: Jamalzadeh, Moeid, Cuniberto, Edoardo, Huang, Zhujun, Feeley, Ryan M, Patel, Jyoti C, Rice, Margaret E, Uichanco, Joline, Shahrjerdi, Davood
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 15.04.2024
• Subjects: Analytical chemistry; Brain; Carbon; Carbon Fiber; Carbon fibers; Chemical sensors; Dopamine; Electrochemical analysis; Electrochemical Techniques - methods; Error analysis; Graphite; Low concentrations; Microelectrodes; Mixtures; Prediction models; Robustness; Sensors; Serotonin; Signal monitoring; Voltammetry; Waveforms
• ISSN: 0003-2654; 1364-5528
• DOI: 10.1039/d3an02086j

Monitoring the coordinated signaling of dopamine (DA) and serotonin (5-HT) is important for advancing our understanding of the brain. However, the co-detection and robust quantification of these signals at low concentrations is yet to be demonstrated. Here, we present the quantification of DA and 5-HT using nano-graphitic (NG) sensors together with fast-scan cyclic voltammetry (FSCV) employing an engineered N-shape potential waveform. Our method yields 6% error in quantifying DA and 5-HT analytes present in in vitro mixtures at concentrations below 100 nM. This advance is due to the electrochemical properties of NG sensors which, in combination with the engineered FSCV waveform, provided distinguishable cyclic voltammograms (CVs) for DA and 5-HT. We also demonstrate the generalizability of the prediction model across different NG sensors, which arises from the consistent voltammetric fingerprints produced by our NG sensors. Curiously, the proposed engineered waveform also improves the distinguishability of DA and 5-HT CVs obtained from traditional carbon fiber (CF) microelectrodes. Nevertheless, this improved distinguishability of CVs obtained from CF is inferior to that of NG sensors, arising from differences in the electrochemical properties of the sensor materials. Our findings demonstrate the potential of NG sensors and our proposed FSCV waveform for future brain studies. The electrochemical properties of nanographitic sensors, together with an engineered waveform, yields distinguishable dopamine and serotonin fingerprints using fast-scan cyclic voltammetry, enabling co-quantification at sub-100 nM concentrations.